About the role
Responsibilities:
- Design end-to-end architecture for mid-sized AI applications.
- Work on Model Optimization: Assist DS in basic fine-tuning jobs (LoRA/QLORA) and optimize inference latency.
- Evaluate and select the right model (commercial vs. open source) for cost/performance trade-offs.
- Mentor Grade 8/9 engineers on AI-native workflows.
- Proven ability to ship ML/AI-powered apps.
- Understanding of system design and ML serving infrastructure.
Core Responsibilities (All Levels):
- Applied GenAI Development: Build production-grade applications using LLMs (GPT-4 Gemini, Llama 3 FK-GPTs).
- Implement RAG (Retrieval Augmented Generation), agentic workflows, and tool-use patterns.
- End-to-End Engineering: Act as the primary engineering owner for non-tech stakeholders (e. g., automating invoice processing for finance).
- You will handle the full SDLC: database design, API development, UI integration, and deployment.
- Model Optimization: Collaborate with data scientists to make AI feasible at scale.
- Work on model distillation, quantization, and inference optimization (e. g., vLLM, etc. ). AI-First SDLC: Actively use AI tools to automate code reviews, documentation, unit testing, and integration testing.